{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas.core.api import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "from pyreadstat import pyreadstat\n",
    "import mytools\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0,metadata=pyreadstat.read_sav(R\"C:\\Users\\23864\\Documents\\Tencent Files\\2386428305\\FileRecv\\企业形象调查原始数据.sav\",\n",
    "apply_value_formats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=mytools.read_spss(R\"C:\\Users\\23864\\Documents\\Tencent Files\\2386428305\\FileRecv\\企业形象调查原始数据.sav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#清理数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=df1[df1.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df1.dropna(thresh=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp1=df2[df2.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=df2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#变量类型设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df3.drop_duplicates(subset=['问卷编号'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                 0\n",
       "问卷编号       float64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
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       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
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     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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       "      <th>肯德基就职意愿</th>\n",
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       "      <th>必胜客企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "问卷编号         int32\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格       float64\n",
       "促销活动       float64\n",
       "产品种类       float64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
       "德克士综合评价   category\n",
       "肯德基企业认知度  category\n",
       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
       "麦当劳综合评价   category\n",
       "必胜客企业认知度  category\n",
       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.astype({'问卷编号':'int'})\n",
    "df5.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性      211\n",
      "女性      204\n",
      "22.0      1\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     138\n",
      "九百到一千二    120\n",
      "一千二以上      85\n",
      "三百到六百      73\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       136\n",
      "德克士        78\n",
      "从不去快餐店     77\n",
      "麦当劳        62\n",
      "必胜客        44\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    188\n",
      "时尚新颖     91\n",
      "中西合璧     88\n",
      "古板单调     32\n",
      "其他类型     17\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般     224\n",
      "不错     139\n",
      "很好      33\n",
      "不好      17\n",
      "0.0      3\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    123\n",
      "只知道名字      120\n",
      "只知道几项       99\n",
      "大致了解        65\n",
      "非常了解         7\n",
      "0.0          2\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     161\n",
      "从未看过     113\n",
      "有时会看到     91\n",
      "常常会看到     51\n",
      "0.0        0\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            167\n",
      "到该公司也不错        118\n",
      "不知道             90\n",
      "一定想办法到该公司工作     40\n",
      "0.0              1\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      154\n",
      "不知道      111\n",
      "买了也不错    100\n",
      "一定会买      50\n",
      "0.0        1\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     166\n",
      "一流      84\n",
      "三流      84\n",
      "不知道     81\n",
      "0.0      1\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道几项      129\n",
      "只知道名字      128\n",
      "大致了解       104\n",
      "一点印象都没有     33\n",
      "非常了解        18\n",
      "0.0          4\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "常常会看到    118\n",
      "有时会看到    112\n",
      "从未看过      45\n",
      "0.0        1\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        143\n",
      "不想去            141\n",
      "不知道             85\n",
      "一定想办法到该公司工作     44\n",
      "0.0              3\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    140\n",
      "不想买      117\n",
      "不知道      102\n",
      "一定会买      54\n",
      "0.0        3\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     198\n",
      "一流     131\n",
      "不知道     52\n",
      "三流      32\n",
      "0.0      3\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      150\n",
      "只知道名字      128\n",
      "大致了解        89\n",
      "一点印象都没有     38\n",
      "非常了解         6\n",
      "0.0          5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "偶尔看到     142\n",
      "有时会看到    127\n",
      "常常会看到     99\n",
      "从未看过      43\n",
      "0.0        4\n",
      "5.0        1\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            147\n",
      "到该公司也不错        145\n",
      "不知道             87\n",
      "一定想办法到该公司工作     33\n",
      "0.0              4\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      135\n",
      "不知道      121\n",
      "买了也不错    120\n",
      "一定会买      36\n",
      "0.0        4\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     170\n",
      "一流     126\n",
      "不知道     79\n",
      "三流      38\n",
      "0.0      3\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      148\n",
      "只知道几项      106\n",
      "一点印象都没有     80\n",
      "大致了解        68\n",
      "非常了解        12\n",
      "0.0          2\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "有时会看到    118\n",
      "从未看过      87\n",
      "常常会看到     70\n",
      "0.0        1\n",
      "5.0        0\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            161\n",
      "不知道            124\n",
      "到该公司也不错         97\n",
      "一定想办法到该公司工作     29\n",
      "0.0              5\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      149\n",
      "不想买      130\n",
      "买了也不错     95\n",
      "一定会买      38\n",
      "0.0        4\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     150\n",
      "一流     114\n",
      "不知道     90\n",
      "三流      58\n",
      "0.0      4\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in df5.columns[df5.dtypes=='category']:\n",
    "    print(df5[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性      211\n",
      "女性      204\n",
      "22.0      1\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     138\n",
      "九百到一千二    120\n",
      "一千二以上      85\n",
      "三百到六百      73\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       136\n",
      "德克士        78\n",
      "从不去快餐店     77\n",
      "麦当劳        62\n",
      "必胜客        44\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    188\n",
      "时尚新颖     91\n",
      "中西合璧     88\n",
      "古板单调     32\n",
      "其他类型     17\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般     224\n",
      "不错     139\n",
      "很好      33\n",
      "不好      17\n",
      "0.0      3\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    123\n",
      "只知道名字      120\n",
      "只知道几项       99\n",
      "大致了解        65\n",
      "非常了解         7\n",
      "0.0          2\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     161\n",
      "从未看过     113\n",
      "有时会看到     91\n",
      "常常会看到     51\n",
      "0.0        0\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            167\n",
      "到该公司也不错        118\n",
      "不知道             90\n",
      "一定想办法到该公司工作     40\n",
      "0.0              1\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      154\n",
      "不知道      111\n",
      "买了也不错    100\n",
      "一定会买      50\n",
      "0.0        1\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     166\n",
      "一流      84\n",
      "三流      84\n",
      "不知道     81\n",
      "0.0      1\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道几项      129\n",
      "只知道名字      128\n",
      "大致了解       104\n",
      "一点印象都没有     33\n",
      "非常了解        18\n",
      "0.0          4\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "常常会看到    118\n",
      "有时会看到    112\n",
      "从未看过      45\n",
      "0.0        1\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        143\n",
      "不想去            141\n",
      "不知道             85\n",
      "一定想办法到该公司工作     44\n",
      "0.0              3\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    140\n",
      "不想买      117\n",
      "不知道      102\n",
      "一定会买      54\n",
      "0.0        3\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     198\n",
      "一流     131\n",
      "不知道     52\n",
      "三流      32\n",
      "0.0      3\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      150\n",
      "只知道名字      128\n",
      "大致了解        89\n",
      "一点印象都没有     38\n",
      "非常了解         6\n",
      "0.0          5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "偶尔看到     142\n",
      "有时会看到    127\n",
      "常常会看到     99\n",
      "从未看过      43\n",
      "0.0        4\n",
      "5.0        1\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            147\n",
      "到该公司也不错        145\n",
      "不知道             87\n",
      "一定想办法到该公司工作     33\n",
      "0.0              4\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      135\n",
      "不知道      121\n",
      "买了也不错    120\n",
      "一定会买      36\n",
      "0.0        4\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     170\n",
      "一流     126\n",
      "不知道     79\n",
      "三流      38\n",
      "0.0      3\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      148\n",
      "只知道几项      106\n",
      "一点印象都没有     80\n",
      "大致了解        68\n",
      "非常了解        12\n",
      "0.0          2\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "有时会看到    118\n",
      "从未看过      87\n",
      "常常会看到     70\n",
      "0.0        1\n",
      "5.0        0\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            161\n",
      "不知道            124\n",
      "到该公司也不错         97\n",
      "一定想办法到该公司工作     29\n",
      "0.0              5\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      149\n",
      "不想买      130\n",
      "买了也不错     95\n",
      "一定会买      38\n",
      "0.0        4\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     150\n",
      "一流     114\n",
      "不知道     90\n",
      "三流      58\n",
      "0.0      4\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "mytools.print_all_cats(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('性别==22.0')\n",
    "df5.loc[322,'性别']='男性'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('服务态度==0.0')\n",
    "df5.loc[101,'服务态度']='不错'\n",
    "df5.loc[197,'服务态度']='不错'\n",
    "df5.loc[258,'服务态度']='不错'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('德克士企业认知度==0.0')\n",
    "df5.loc[220,'德克士企业认知度']='大致了解'\n",
    "df5.loc[357,'德克士企业认知度']='大致了解'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('德克士就职意愿==0.0')\n",
    "df5.loc[223,'德克士就职意愿']='不想去'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('德克士股票==0.0')\n",
    "df5.loc[223,'德克士股票']='不想买'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('德克士综合评价==0.0')\n",
    "df5.loc[223,'德克士综合评价']='二流'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('肯德基企业认知度==0.0')\n",
    "df5.loc[352,'肯德基企业认知度']='大致了解'\n",
    "df5.loc[353,'肯德基企业认知度']='大致了解'\n",
    "df5.loc[357,'肯德基企业认知度']='大致了解'\n",
    "df5.loc[359,'肯德基企业认知度']='大致了解'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('肯德基广告接触度==0.0')\n",
    "df5.loc[223,'肯德基广告接触度']='常常会看到'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('肯德基就职意愿==0.0')\n",
    "df5.loc[352,'肯德基就职意愿']='到该公司也不错'\n",
    "df5.loc[353,'肯德基就职意愿']='到该公司也不错'\n",
    "df5.loc[359,'肯德基就职意愿']='到该公司也不错'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('肯德基股票==0.0')\n",
    "df5.loc[223,'肯德基股票']='买了也不错'\n",
    "df5.loc[352,'肯德基股票']='买了也不错'\n",
    "df5.loc[353,'肯德基股票']='买了也不错'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('肯德基综合评价==0.0')\n",
    "df5.loc[223,'肯德基综合评价']='一流'\n",
    "df5.loc[352,'肯德基综合评价']='一流'\n",
    "df5.loc[353,'肯德基综合评价']='一流'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('麦当劳企业认知度==0.0')\n",
    "df5.loc[223,'麦当劳企业认知度']='只知道几项'\n",
    "df5.loc[352,'麦当劳企业认知度']='只知道名字'\n",
    "df5.loc[353,'麦当劳企业认知度']='只知道名字'\n",
    "df5.loc[357,'麦当劳企业认知度']='大致了解'\n",
    "df5.loc[361,'麦当劳企业认知度']='大致了解'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('麦当劳广告接触度==0')\n",
    "df5.loc[220,'麦当劳广告接触度']='有时会看到'\n",
    "df5.loc[352,'麦当劳广告接触度']='有时会看到'\n",
    "df5.loc[353,'麦当劳广告接触度']='有时会看到'\n",
    "df5.loc[351,'麦当劳广告接触度']='有时会看到'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('麦当劳广告接触度==5')\n",
    "df5.loc[404,'麦当劳广告接触度']='从未看过'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('麦当劳就职意愿==0')\n",
    "df5.loc[222,'麦当劳就职意愿']='不知道'\n",
    "df5.loc[352,'麦当劳就职意愿']='不知道'\n",
    "df5.loc[353,'麦当劳就职意愿']='不知道'\n",
    "df5.loc[357,'麦当劳就职意愿']='不知道'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('麦当劳股票==0.0')\n",
    "df5.loc[223,'麦当劳股票']='买了也不错'\n",
    "df5.loc[352,'麦当劳股票']='不想买'\n",
    "df5.loc[353,'麦当劳股票']='不想买'\n",
    "df5.loc[357,'麦当劳股票']='不想买'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('麦当劳综合评价==0.0')\n",
    "df5.loc[352,'麦当劳综合评价']='二流'\n",
    "df5.loc[353,'麦当劳综合评价']='二流'\n",
    "df5.loc[357,'麦当劳综合评价']='二流'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('必胜客企业认知度==0.0')\n",
    "df5.loc[352,'必胜客企业认知度']='大致了解'\n",
    "df5.loc[353,'必胜客企业认知度']='大致了解'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('必胜客广告接触度==0.0')\n",
    "df5.loc[352,'必胜客广告接触度']='有时会看到'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('必胜客就职意愿==0.0')\n",
    "df5.loc[222,'必胜客就职意愿']='不想去'\n",
    "df5.loc[223,'必胜客就职意愿']='一定想办法到该公司工作'\n",
    "df5.loc[352,'必胜客就职意愿']='不知道'\n",
    "df5.loc[353,'必胜客就职意愿']='不知道'\n",
    "df5.loc[357,'必胜客就职意愿']='不知道'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('必胜客股票==0.0')\n",
    "df5.loc[222,'必胜客股票']='不想买'\n",
    "df5.loc[352,'必胜客股票']='买了也不错'\n",
    "df5.loc[353,'必胜客股票']='买了也不错'\n",
    "df5.loc[357,'必胜客股票']='买了也不错'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('必胜客综合评价==0.0')\n",
    "df5.loc[222,'必胜客综合评价']='三流'\n",
    "df5.loc[352,'必胜客综合评价']='二流'\n",
    "df5.loc[353,'必胜客综合评价']='二流'\n",
    "df5.loc[357,'必胜客综合评价']='二流'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       416.000000\n",
       "mean        513.247596\n",
       "std        4893.048164\n",
       "min           1.000000\n",
       "25%         140.750000\n",
       "50%         259.000000\n",
       "75%         392.000000\n",
       "max      100000.000000\n",
       "Name: 问卷编号, dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['问卷编号'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count       416.000000\n",
      "mean        513.247596\n",
      "std        4893.048164\n",
      "min           1.000000\n",
      "25%         140.750000\n",
      "50%         259.000000\n",
      "75%         392.000000\n",
      "max      100000.000000\n",
      "Name: 问卷编号, dtype: float64\n",
      "count    416.000000\n",
      "mean      24.127404\n",
      "std        6.501946\n",
      "min       10.000000\n",
      "25%       20.000000\n",
      "50%       22.000000\n",
      "75%       26.000000\n",
      "max       60.000000\n",
      "Name: 年龄, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.281250\n",
      "std        0.450151\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 欢乐聚餐, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.137019\n",
      "std        0.344282\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 快乐童年, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.293269\n",
      "std        0.455809\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 都市生活, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.170673\n",
      "std        0.598959\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max       10.000000\n",
      "Name: 洋溢青春, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.192308\n",
      "std        0.429669\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        4.000000\n",
      "Name: 其他感受, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.151442\n",
      "std        0.358911\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 健康安全, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.572115\n",
      "std        0.495368\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        1.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速便捷, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.314904\n",
      "std        0.465037\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 价格昂贵, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.165865\n",
      "std        0.372408\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 口感不好, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.163462\n",
      "std        0.370231\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 品类太少, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.076923\n",
      "std        0.266790\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他评价, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.456731\n",
      "std        0.498724\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速好吃, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.192308\n",
      "std        0.394588\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 服务好, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 种类多, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.168269\n",
      "std        0.374556\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 价格便宜, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.086538\n",
      "std        0.281496\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他吸引因素, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.319712\n",
      "std        0.466926\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 服务态度需改进, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 送餐速度, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.358173\n",
      "std        0.480041\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 就餐环境, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.391827\n",
      "std        0.488746\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 产品价格, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.237981\n",
      "std        0.426360\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 促销活动, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 产品种类, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.045673\n",
      "std        0.209026\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他改进因素, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "mytools.print_all_int(df5)\n",
    "mytools.print_all_float(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = '德克士企业认知度 == \"一点印象都没有\" and 德克士综合评价 == \"一流\"'\n",
    "temp2 = df5.query(c1)[['德克士企业认知度','德克士综合评价']]\n",
    "df6 = df5.drop(temp2.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "c2 = '肯德基企业认知度 == \"一点印象都没有\" and 肯德基综合评价 == \"一流\"'\n",
    "temp3 = df6.query(c2)[['肯德基企业认知度','肯德基综合评价']]\n",
    "df7 = df6.drop(temp3.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "c3 = '麦当劳企业认知度 == \"一点印象都没有\" and 麦当劳综合评价 == \"一流\"'\n",
    "temp4 = df7.query(c3)[['麦当劳企业认知度','麦当劳综合评价']]\n",
    "df8 = df7.drop(temp4.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "c4 = '必胜客企业认知度 == \"一点印象都没有\" and 必胜客综合评价 == \"一流\"'\n",
    "temp5 = df8.query(c4)[['必胜客企业认知度','必胜客综合评价']]\n",
    "df9 = df8.drop(temp5.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df9.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#描述统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = df['服务态度'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>服务态度</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>一般</td>\n",
       "      <td>199</td>\n",
       "      <td>54.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>不错</td>\n",
       "      <td>118</td>\n",
       "      <td>32.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>很好</td>\n",
       "      <td>31</td>\n",
       "      <td>8.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>不好</td>\n",
       "      <td>15</td>\n",
       "      <td>4.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>总和</td>\n",
       "      <td>363</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  服务态度   个数    百分比\n",
       "0   一般  199  54.82\n",
       "1   不错  118  32.51\n",
       "2   很好   31   8.54\n",
       "3   不好   15   4.13\n",
       "4  0.0    0    0.0\n",
       "5   总和  363  100.0"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.DataFrame()\n",
    "b['服务态度'] = df['服务态度'].value_counts().index\n",
    "b['个数'] = df['服务态度'].value_counts().values\n",
    "b['百分比'] = df['服务态度'].value_counts(normalize=True).values * 100\n",
    "b['百分比'] = b['百分比'].apply(lambda x: round(x, 2))\n",
    "total_row = pd.Series({'服务态度':'总和','个数':b['个数'].sum(),'百分比':b['百分比'].sum()}).to_frame().T\n",
    "pd.concat([b,total_row],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>服务态度</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>一般</td>\n",
       "      <td>199</td>\n",
       "      <td>54.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>不错</td>\n",
       "      <td>118</td>\n",
       "      <td>32.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>很好</td>\n",
       "      <td>31</td>\n",
       "      <td>8.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>不好</td>\n",
       "      <td>15</td>\n",
       "      <td>4.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>总和</td>\n",
       "      <td>363</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  服务态度   个数    百分比\n",
       "0   一般  199  54.82\n",
       "1   不错  118  32.51\n",
       "2   很好   31   8.54\n",
       "3   不好   15   4.13\n",
       "4  0.0    0    0.0\n",
       "5   总和  363  100.0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df,'服务态度')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女性</td>\n",
       "      <td>182</td>\n",
       "      <td>50.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>男性</td>\n",
       "      <td>181</td>\n",
       "      <td>49.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>总和</td>\n",
       "      <td>363</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     性别   个数    百分比\n",
       "0    女性  182  50.14\n",
       "1    男性  181  49.86\n",
       "2  22.0    0    0.0\n",
       "3    总和  363  100.0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df,'性别')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'伙食费')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>服务态度</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "      <th>累计百分比(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>一般</td>\n",
       "      <td>199</td>\n",
       "      <td>54.82</td>\n",
       "      <td>54.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>不错</td>\n",
       "      <td>118</td>\n",
       "      <td>32.51</td>\n",
       "      <td>87.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>很好</td>\n",
       "      <td>31</td>\n",
       "      <td>8.54</td>\n",
       "      <td>95.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>不好</td>\n",
       "      <td>15</td>\n",
       "      <td>4.13</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总和</td>\n",
       "      <td>363</td>\n",
       "      <td>100.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  服务态度   个数    百分比 累计百分比(%)\n",
       "0   一般  199  54.82    54.82\n",
       "1   不错  118  32.51    87.33\n",
       "2   很好   31   8.54    95.87\n",
       "3   不好   15   4.13    100.0\n",
       "4   总和  363  100.0         "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.api.types import CategoricalDtype\n",
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['一般','不错', '很好','不好'], ordered=True)\n",
    "df = df.astype({'服务态度':cat_dtype})\n",
    "mytools.ordinal_desc(df,'服务态度')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'服务态度',sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    363.000000\n",
       "mean       8.118457\n",
       "std        2.227345\n",
       "min        3.000000\n",
       "25%        7.000000\n",
       "50%        8.000000\n",
       "75%       10.000000\n",
       "max       13.000000\n",
       "Name: 认知维度, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['认知维度']= df['德克士类型感知'].cat.codes + df['德克士企业认知度'].cat.codes + df['德克士综合评价'].cat.codes\n",
    "df['认知维度'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='认知维度', ylabel='Count'>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.histplot(data=df, x=\"认知维度\",binwidth=1,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.crosstab(\n",
    "        df['服务态度'],\n",
    "        df['德克士综合评价'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>德克士综合评价</th>\n",
       "      <th>一流</th>\n",
       "      <th>三流</th>\n",
       "      <th>不知道</th>\n",
       "      <th>二流</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一般</th>\n",
       "      <td>34.09</td>\n",
       "      <td>61.73</td>\n",
       "      <td>62.82</td>\n",
       "      <td>53.12</td>\n",
       "      <td>54.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>不错</th>\n",
       "      <td>43.18</td>\n",
       "      <td>24.69</td>\n",
       "      <td>20.51</td>\n",
       "      <td>39.38</td>\n",
       "      <td>32.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>很好</th>\n",
       "      <td>20.45</td>\n",
       "      <td>6.17</td>\n",
       "      <td>10.26</td>\n",
       "      <td>5.62</td>\n",
       "      <td>8.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>不好</th>\n",
       "      <td>2.27</td>\n",
       "      <td>7.41</td>\n",
       "      <td>6.41</td>\n",
       "      <td>1.88</td>\n",
       "      <td>4.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "德克士综合评价     一流     三流    不知道     二流     合计\n",
       "服务态度                                      \n",
       "一般       34.09  61.73  62.82  53.12  54.82\n",
       "不错       43.18  24.69  20.51  39.38  32.51\n",
       "很好       20.45   6.17  10.26   5.62   8.54\n",
       "不好        2.27   7.41   6.41   1.88   4.13"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tau_y值为:0.026，该值属于极弱相关或不相关。'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_y = mytools.goodmanKruska_tau_y(df, '服务态度', '德克士综合评价')\n",
    "f'tau_y值为:{tau_y:.3f}，该值属于{mytools.draw_on_corr(tau_y)}。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>服务态度</th>\n",
       "      <th>一般</th>\n",
       "      <th>不错</th>\n",
       "      <th>很好</th>\n",
       "      <th>不好</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士综合评价</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一流</th>\n",
       "      <td>7.54</td>\n",
       "      <td>16.10</td>\n",
       "      <td>29.03</td>\n",
       "      <td>6.67</td>\n",
       "      <td>12.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三流</th>\n",
       "      <td>25.13</td>\n",
       "      <td>16.95</td>\n",
       "      <td>16.13</td>\n",
       "      <td>40.00</td>\n",
       "      <td>22.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>不知道</th>\n",
       "      <td>24.62</td>\n",
       "      <td>13.56</td>\n",
       "      <td>25.81</td>\n",
       "      <td>33.33</td>\n",
       "      <td>21.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二流</th>\n",
       "      <td>42.71</td>\n",
       "      <td>53.39</td>\n",
       "      <td>29.03</td>\n",
       "      <td>20.00</td>\n",
       "      <td>44.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "服务态度        一般     不错     很好     不好     合计\n",
       "德克士综合评价                                   \n",
       "一流        7.54  16.10  29.03   6.67  12.12\n",
       "三流       25.13  16.95  16.13  40.00  22.31\n",
       "不知道      24.62  13.56  25.81  33.33  21.49\n",
       "二流       42.71  53.39  29.03  20.00  44.08"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['一般','不错', '很好','不好'], ordered=True)\n",
    "df = df.astype({'服务态度':cat_dtype})\n",
    "\n",
    "result = pd.crosstab(\n",
    "        df['德克士综合评价'],\n",
    "        df['服务态度'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100\n",
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.06062965558410548"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "x = df['服务态度'].cat.codes\n",
    "y = df['德克士综合评价'].cat.codes\n",
    "dy = stats.somersd(x, y)\n",
    "dy.statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'萨莫司dy值为:-0.061，该值属于极弱相关或不相关,p值为0.224,接收虚无假设，拒绝研究假设。'"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = dy.pvalue\n",
    "f\"萨莫司dy值为:{dy.statistic:.3f}，该值属于{mytools.draw_on_corr(dy.statistic)},p值为{p:.3f},{mytools.p_result(p)['conclusion']}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['情感维度']= df['德克士广告接触度'].cat.codes + df['德克士股票'].cat.codes + df['德克士就职意愿'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.gen_scatter(df,'情感维度','认知维度')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'积矩相关系数r为:0.193,决定系数r平方为:0.037，相关强度为极弱相关或不相关。'"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r, p = stats.pearsonr(df['情感维度'], df['认知维度'])\n",
    "f\"积矩相关系数r为:{r:.3f},决定系数r平方为:{r*r:.3f}，相关强度为{mytools.draw_on_r(r*r)}。\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(\n",
    "        x='常去的快餐店',\n",
    "        y='认知维度',\n",
    "        data=df,\n",
    "        color='white',\n",
    "        linewidth=1,\n",
    "        width=0.5,\n",
    "    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'相关比率为：0.039,按照J.Cohen 提出的标准(0.01时为小效应,0.06时为中等效应,而0.14为大效应)，强度为低度相关。'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "model = ols('认知维度 ~ 常去的快餐店', df).fit()\n",
    "eta_2 = model.rsquared\n",
    "f\"\"\"相关比率为：{eta_2:.3f},按照J.Cohen 提出的标准(0.01时为小效应,0.06时为中等效应,而0.14为大效应)，强度为{mytools.draw_on_eta2(eta_2)}。\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = mytools.set_label_to_code(df, metadata)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "138148c979a60859ae74ca41993c9becbe8ce800154b30dc52652dbd6e25207c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
